Comments (3)
dsleo
commented on June 15, 2024
Hello,
Thank you for a really cool repo ! Just like @sezan92 I'm not sure of certain things regarding your attention for LSTM.
Specifically, it's not clear why you would permute time_steps and input_dim in here ?
Looking at this feed forward attention for a reference, we would want to apply a dense layer on top of the matrix of shape (timestep,input_dim) so that we actually multiply each hidden state h_i by the weight matrix.
Am I missing something here?
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fyubang
commented on June 15, 2024
Hello,
Thanks for the code.
but I agree with @sezan92 . I think that it should use the hidden state to calculate the attention.
Thanks in advance for responses.
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philipperemy
commented on June 15, 2024
Thanks for your interest! I updated the code to reflect some changes based on this thread: #14. Let me know if you still have some concerns :)
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Related Issues (20)
- Restricting attention weights to domain
- Hiddent state parameter, what really should be passed? HOT 1
- pip install and numpy, keras packages are forced to be uninstalled HOT 1
- Use this repository for CNN HOT 1
- 2D attention HOT 6
- weird attention weights when adding sequence of numbers. HOT 1
- attention when using more than one feature HOT 1
- get_config HOT 14
- Using attention with multivariate timeseries data
- Loading model problems HOT 5
- Interpreting attention weights for more than one input features. HOT 2
- Add guidance to README to use Functional API for saving models that use this layer HOT 4
- Attention Mechanism not working HOT 10
- what do the h_t mean in the Attention model? HOT 1
- Output with multiple time steps HOT 1
- Attention not working for MLP HOT 2
- TypeError: Expected `trainable` argument to be a boolean, but got: 64 HOT 3
- Please update version HOT 1
- TypeError: __call__() takes 2 positional arguments but 3 were given HOT 2
- Number of parameters in Attention layer HOT 2
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